Convolutional neural networks(CNNs)exhibit superior performance in image feature extraction,making them extensively used in the area of traffic sign recognition.However,the design of existing traffic sign recognition ...Convolutional neural networks(CNNs)exhibit superior performance in image feature extraction,making them extensively used in the area of traffic sign recognition.However,the design of existing traffic sign recognition algorithms often relies on expert knowledge to enhance the image feature extraction networks,necessitating image preprocessing and model parameter tuning.This increases the complexity of the model design process.This study introduces an evolutionary neural architecture search(ENAS)algorithm for the automatic design of neural network models tailored for traffic sign recognition.By integrating the construction parameters of residual network(ResNet)into evolutionary algorithms(EAs),we automatically generate lightweight networks for traffic sign recognition,utilizing blocks as the fundamental building units.Experimental evaluations on the German traffic sign recognition benchmark(GTSRB)dataset reveal that the algorithm attains a recognition accuracy of 99.32%,with a mere 2.8×10^(6)parameters.Experimental results comparing the proposed method with other traffic sign recognition algorithms demonstrate that the method can more efficiently discover neural network architectures,significantly reducing the number of network parameters while maintaining recognition accuracy.展开更多
Primary light chain amyloidosis is a rare hematologic disease with multi-organ involvement.Nearly one-third of patients with amyloidosis experience five or more consultations before diagnosis,which may lead to a poor ...Primary light chain amyloidosis is a rare hematologic disease with multi-organ involvement.Nearly one-third of patients with amyloidosis experience five or more consultations before diagnosis,which may lead to a poor prognosis due to delayed diagnosis.Early risk prediction based on artificial intelligence is valuable for clinical diagnosis and treatment of amyloidosis.For this disease,we propose an Evolutionary Neural Architecture Searching(ENAS)based risk prediction model,which achieves high-precision early risk prediction using physical examination data as a reference factor.To further enhance the value of clinic application,we designed a natural language-based interpretable system around the NAS-assisted risk prediction model for amyloidosis,which utilizes a large language model and Retrieval-Augmented Generation(RAG)to achieve further interpretation of the predicted conclusions.We also propose a document-based global semantic slicing approach in RAG to achievemore accurate slicing and improve the professionalism of the generated interpretations.Tests and implementation show that the proposed risk prediction model can be effectively used for early screening of amyloidosis and that the interpretation method based on the large language model and RAG can effectively provide professional interpretation of predicted results,which provides an effective method and means for the clinical applications of AI.展开更多
基金supported by the National Natural Science Foundation of China(No.62066041).
文摘Convolutional neural networks(CNNs)exhibit superior performance in image feature extraction,making them extensively used in the area of traffic sign recognition.However,the design of existing traffic sign recognition algorithms often relies on expert knowledge to enhance the image feature extraction networks,necessitating image preprocessing and model parameter tuning.This increases the complexity of the model design process.This study introduces an evolutionary neural architecture search(ENAS)algorithm for the automatic design of neural network models tailored for traffic sign recognition.By integrating the construction parameters of residual network(ResNet)into evolutionary algorithms(EAs),we automatically generate lightweight networks for traffic sign recognition,utilizing blocks as the fundamental building units.Experimental evaluations on the German traffic sign recognition benchmark(GTSRB)dataset reveal that the algorithm attains a recognition accuracy of 99.32%,with a mere 2.8×10^(6)parameters.Experimental results comparing the proposed method with other traffic sign recognition algorithms demonstrate that the method can more efficiently discover neural network architectures,significantly reducing the number of network parameters while maintaining recognition accuracy.
基金supported by Liaoning Province Key R&D Program Project(Grant Nos.2019JH2/10100027)in part by Grants from Shenyang Science and Technology Plan Project(Grant No.RC210469).
文摘Primary light chain amyloidosis is a rare hematologic disease with multi-organ involvement.Nearly one-third of patients with amyloidosis experience five or more consultations before diagnosis,which may lead to a poor prognosis due to delayed diagnosis.Early risk prediction based on artificial intelligence is valuable for clinical diagnosis and treatment of amyloidosis.For this disease,we propose an Evolutionary Neural Architecture Searching(ENAS)based risk prediction model,which achieves high-precision early risk prediction using physical examination data as a reference factor.To further enhance the value of clinic application,we designed a natural language-based interpretable system around the NAS-assisted risk prediction model for amyloidosis,which utilizes a large language model and Retrieval-Augmented Generation(RAG)to achieve further interpretation of the predicted conclusions.We also propose a document-based global semantic slicing approach in RAG to achievemore accurate slicing and improve the professionalism of the generated interpretations.Tests and implementation show that the proposed risk prediction model can be effectively used for early screening of amyloidosis and that the interpretation method based on the large language model and RAG can effectively provide professional interpretation of predicted results,which provides an effective method and means for the clinical applications of AI.
